Abstract:In
this work, we model the sensor networks as an unsupervised learning and
clustering process. We classify nodes according to its static distribution to
form known class densities (CCPD). These densities are chosen from specific
cross-layer features which maximizes lifetime of power-aware routing algorithms.
To circumvent computational complexities of a power-ware communication STACK we
introduce path-loss models at the nodes only for high density deployments. We
study the cluster heads and formulate the data handling capacity for an expected
deployment and use localized probability models to fuse the data with its side
information before transmission. So each cluster head has a unique Pmax
but not all cluster heads have the same measured value.

In a
lossless mode if there are no faults in the sensor network then we can show that
the highest probability given by Pmax is ambiguous if its frequency
is ≤ n/2 otherwise it can be determined by a local function. We further
show that the event detection at the cluster heads can be modelled with a
pattern 2m and m, the number of bits can be a correlated pattern of
2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy
< 1.

These
local algorithms are further studied to optimize on power, fault detection and
to maximize on the distributed routing algorithm used at the higher layers. From
these bounds in large network, it is observed that the power dissipation is
network size invariant. The performance of the routing algorithms solely based
on success of finding healthy nodes in a large distribution. It is also observed
that if the network size is kept constant and the density of the nodes is kept
closer then the local pathloss model effects the performance of the routing
algorithms. We also obtain the maximum intensity of transmitting nodes for a
given category of routing algorithms for an outage constraint, i.e., the
lifetime of sensor network.